Email this article 
Open Access
Subscription or Fee Access
Inferring DNA Appearance Statistics for Discovering Oral Cancer Using Dynamic Bayesian Network with Different Entrant Biomarkers
Abstract
Oral wilder will arise at intervals the pinnacle and neck region. Thanks to the aggressive nature of the unwellness, which often ends up in poor prognosis, Oral Epithelial Cell Malignant Neoplastic Disease (OSCC) constitutes the eighth commonest neoplasm in humans. Within the gift work we’ve an inclination to formulate issue interaction network from carcinoma genomic data exploitation Dynamic Theorem Networks (DBNs). Four modules were extracted once applying a method to the network. We’ve an inclination to consequently explore them by applying topological and purposeful analysis ways so as to identify very important network nodes. Our analysis discovered that these very important nodes might correspond to candidate biomarkers of the unwellness. Index Terms—Oral cancer, Dynamic theorem Networks, Oral epithelial cell malignant neoplastic disease.
References
Y. Safdari, M. Khalili, S. Farajnia, M. Asgharzadeh, Y. Yazdani, andM. Sadeghi, "Recent advances in head and neck squamous cellcarcinoma—A review," Clinical biochemistry, vol. 47, pp. 1195-1202, 2014.
W. M. Mendenhall, J. W. Werning, and D. G. Pfister, "Treatment ofhead and neck cancer," DeVita VT Jr, Lawrence TS, RosenbergSA: Cancer: Principles and Practice of Oncology. 9th ed Philadelphia, Pa: Lippincott Williams & Wilkins, pp. 729-80, 2011.
A. Forastiere, R. Weber, and K. Ang, "Treatment of head and neckcancer," N Engl J Med, vol. 358, p. 1076, 2008.
W. Yang, K. Yoshigoe, X. Qin, J. S. Liu, J. Y. Yang, A. Niemierko,Y. Deng, Y. Liu, A. K. Dunker, and Z. Chen, "Identification ofgenes and pathways involved in kidney renal clear cell carcinoma," BMC bioinformatics, vol. 15, p. S2, 2014.
K. Kalantzaki, E. S. Bei, K. P. Exarchos, M. Zervakis, M. Garofalakis,and D. I. Fotiadis, "Nonparametric network design and analysis of disease genes in oral cancer progression," Biomedical and Health Informatics, IEEE Journal of, vol. 18, pp. 562-573, 2014.
M. S. Cline, M. Smoot, E. Cerami, A. Kuchinsky, N. Landys, C.Workman, R. Christmas, I. Avila-Campilo, M. Creech, and B.Gross, "Integration of biological networks and gene expressiondata using Cytoscape," Nature protocols, vol. 2, pp. 2366-2382,2007.
C. L. Estilo, O. Pornchai, S. Talbot, N. D. Socci, D. L. Carlson, R.Ghossein, T. Williams, Y. Yonekawa, Y. Ramanathan, and J. O.Boyle, "Oral tongue cancer gene expression profiling: Identification of novel potential prognosticators byoligonucleotide microarray analysis," BMC cancer, vol. 9, p. 11, 2009.
Y. Wang, J. G. Klijn, Y. Zhang, A. M. Sieuwerts, M. P. Look, F. Yang, D. Talantov, M. Timmermans, M. E. Meijer-van Gelder,and J. Yu, "Gene-expression profiles to predict distant metastasisof lymph-node-negative primary breast cancer," The Lancet, vol.365, pp. 671-679, 2005.
L. J. van't Veer, H. Dai, M. J. Van De Vijver, Y. D. He, A. A. Hart,M. Mao, H. L. Peterse, K. van der Kooy, M. J. Marton, and A.T. Witteveen, "Gene expression profiling predicts clinical outcome of breast cancer," Nature, vol. 415, pp. 530-536, 2002.
G. Wu and L. Stein, "A network module-based method for identifyingcancer prognostic signatures," Genome Biol, vol. 13, p. R112, 2012.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.